Anxious individuals shift emotion control from lateral frontal pole to dorsolateral prefrontal cortex

Anxious individuals consistently fail in controlling emotional behavior, leading to excessive avoidance, a trait that prevents learning through exposure. Although the origin of this failure is unclear, one candidate system involves control of emotional actions, coordinated through lateral frontopolar cortex (FPl) via amygdala and sensorimotor connections. Using structural, functional, and neurochemical evidence, we show how FPl-based emotional action control fails in highly-anxious individuals. Their FPl is overexcitable, as indexed by GABA/glutamate ratio at rest, and receives stronger amygdalofugal projections than non-anxious male participants. Yet, high-anxious individuals fail to recruit FPl during emotional action control, relying instead on dorsolateral and medial prefrontal areas. This functional anatomical shift is proportional to FPl excitability and amygdalofugal projections strength. The findings characterize circuit-level vulnerabilities in anxious individuals, showing that even mild emotional challenges can saturate FPl neural range, leading to a neural bottleneck in the control of emotional action tendencies.

Reviewer #2: Remarks to the Author: I am reviewing the paper "Anxious individuals shift emotion control from lateral frontal pole to dorsal prefrontal cortex; functional, structural and neurochemical evidence" by Bramson et al. In this study, the authors examine lateral frontopolar (FPl) function in control of emotional action using a well validated task and a multi-modal imaging approach, including functional neuroimaging, structural connectivity (DWI), and spectroscopy metrics that reflect the baseline excitability (GABA/Glu ratio) of FPl and PFC regions of interest. The authors contrast FPl & (DL)PFC activation profiles between high-anxious vs. non-anxious individuals, and find that (1) Anxious participants show less "emotion incongruency" activation in FPl compared to non anxious; (2) Anxious participants show instead increased congruency-sensitive activation in dorsolateral PFC (DLPFC) compared to non-anxious individuals; (3) The magnitude of functional engagement of FPl to the incongruency contrast is inversely correlated with anxiety scores, further corroborating finding (#1). Moreover, the authors attempt to relate these distinct profiles of FPl engagement in their task to other trait-like variation in the structure and excitability of FPl. Here, they find that (4) FPl is more excitable in anxious vs. non-anxious individuals; and the magnitude of FPl excitability is differentially associated with the (behavioral) congruency effect in their task as a function of anxiety (such that non-anxious individuals have better emotional control with more excitable FPl; but vice versa for anxious folks). There are additional findings here that were not readily intuitive to me, which included that (5) the behavioral congruency effect was related to GABA/Glu in SMC in the non-anxious group, and that (6) the neural congruency effect in SMC correlated with FPl excitability only in the non-anxious group. When examining FPl structure, the authors found that (7) tamygdalofugal pathway/projections to FPl were stronger in high-anxious individuals and that this group difference was spatially specific to FPl and not amygdalofugal projections to medial PFC (BA24/25); and (8) that the association between amygdalofugal anatomy and emotional control changed between the groups, such that in the non-anxious group only, higher structural connectivity was associated with greater behavioral congruency effects (worse control(. They also note that (9) high anxiety participants show associations between amygdalofugal strength and neural congruency effect in mPFC and ACC. Finally, the authors find that amygdalofugal-FPl strength was associated with greater DLPFC, further corroborating finding (2).
On one hand, the quest of determining regional specificity in the PFC for the cognitive control of emotion is timely and in critical need of new data and careful analysis aimed at that goal. The authors have amassed an impressive amount of data to that end and have prioritized intra-subject reliability (collecting a large number of trials per subject; n>500), which is commendable and rarely done in affective neuroscience. Moreover, they use a task that is well validated by the past decade of their work, a body of work that had raised important questions regarding FPl "helping" or "hindering" regulation, which the present manuscript helps to answer. On the other hand, there were aspects of the current manuscript that I found challenging to parse-conceptually, and I also had a few methodological concerns, which if addressed, could strengthen an already impressive body of work. Figure 2C, I appreciate using Bayesian statistics, but another strong and easily interpretable test is whether there is a significant interaction between Group and the Congruency effect. Same question for Figure 2D: in other words, is FPl (incongruent) significantly more engaged for Non-anxious vs. Anxious, and DLPFC significantly more engaged for Anx vs. Non-Anx?

For finding in
2. An additional (potentially more direct) test of the authors' interesting conjecture regarding DLPFC vs. FPl engagement in the High Anx group is that the magnitude of Inc>Cong in DLPFC should be inversely correlated with the magnitude of Inc>Cong FPl, either in High Anx alone, or potentially across both groups. In other words, if DLPFC engagement is reflecting some sort of compensatory effect, then it should be inversely related to how much FPl is 'up to the task'. Is that the case? And/or is the delta DLPFC-FPl across subjects related to anxiety and/or the emotional congruency effect?
3. The analysis that included Left SMC ( Figures 3C & 3D) (also, why Left and not Right?) did not seem theoretically motivated until I got deep into the Methods section. If the authors consider that set of findings of equal import to the manuscript (vs. being in Supplementary) compared to FPl/DLPFC findings, then additional setting up of the theoretical framework-including prior findings-and stating the hypotheses in the Introduction would be warranted. As is, I found those findings quite difficult to follow and to integrate with FPl and DLPFC results. 4. I found the reverse association between Gaba/Glu and the behavioral congruency effect intriguing ( Fig 3B). It suggests there's a point at which FPl function/engagement is 'counterproductive' for Anx folks. I would have liked to see those findings more directly connected to DLPFC functon( Fig 2B)-are these two separate stories-i.e. are these effects impacting different individuals? Or are the High Anx individuals with more excitable FPl (less helpful) also the ones recruiting DLPFC more during the task (Inc> Con)? Can you enter those IVs (GABA/Glu, DLPFC engagement, FPl engagement, amygdalofugal pathway-FPl) in the same simultaneous regression model predicting Anx symptoms or the behavioral congruency effect to ascertain whether these neural metrics are explaining overlapping variance, or possibly exerting 'suppression' (in a regression framework) effects? (On suppression, see: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3780394/) 5. The gender imbalance across the samples raises questions about how to best address this confound. It was not clear to me exactly how Figure 4B or Suppl. Figure 3 addressed it (though they're mentioned in the Discussion). Is there an additional control sample of non-anxious individuals that includes females that the authors could use for at least a subset of these analysis? (e.g. structural); or vice versa (i.e. a high anxiety group with male subjects only  Figure 3 is quite novel and important; I was surprised to see it in Supplementary. In particular, I would be interested in knowing whether there is an interaction between region (Fpl vs. BA25 and 24) and Group (as for BA24/25 the association with Anx seems numerically reversed). It is also interesting that the amygdalofugal_FPl pathway correlates positively with anxiety, which warrants additional discussion. 7. Figure 4D is very helpful for connecting and interpreting the structural-functional findings across the paper; but here I found Figure 4C difficult to integrate with the rest. Is there spatial overlap between 4C and 4D? How does 4C (medial PFC, also dorsal PFC, interaction with Group and amygdalofugal pathway) help with the interpretation of 4D (greater DLPFC engagement across groups for individuals with greater amygdalofugal pathway to FPl)?  Line 282: I am not sure the present data permit strong inferences  regarding FPl afferents vs efferents; can the authors elaborate on this? 3. Is there spatial overlap between the results shown in 2E and in 2C? If so, that would be worth indicating.
4. I believe there is an error when referring to Figure 4; there is no 4E and I think the lettering is off by one throughout. 5. I do wonder whether the authors examined differential amygdala activation profiles across these Anx vs. Non Anx groups and whether those relate to FPl structural, neurochemical, or functional profiles?
6. This is at surface minor but it is important for interpretation. At times the 'congruence' effects seemed to refer to FPl's increased engagement to incongruent vs. congruent, but later it was referred to primarily as the facilitation of action by emotion. Keeping the labeling, terminology (and contrast direction) consistent throughout the manuscript would facilitate parsing through (and connecting) the present results.
Reviewer #3: Remarks to the Author: Bramson et al. investigate the role of the brain circuit involving the lateral frontolpolar cortex (FPI), posterior parietal cortex, sensorimotor cortex and amygdala in emotion-action control in subjects with anxiety. They used magnetic resonance spectroscopy, diffusion weighted imaging and functional MRI to target FPI GABA/Glx levels, functional activation of FPI during a mild emotional challenge requiring emotional action control in high-anxious (n=52, 14 male) and nonanxious (n=41, all male) participants. Their main findings are the following: relative to nonanxious subjects, high-anxious participants use more dPFC in emotion-action control, have lower FPI GABA/Glx, and show stronger connectivity in amygdalofugal projection to FPI. They conclude that there is a functional-anatomical shift in emotion-action control in anxious subjects, and this shift is associated with the change in structural connectivity of amygdalofugal projections to FPl and overexcitability in FPI. Overall, I find that it is very nice to use a multimodal approach addressing the question from functional, structural and neurochemical aspects, which may provide mechanistic insights. I have a few major comments related to the study methodologies and design, which may affect the results, interpretation and conclusion.
Major comments: 1. A "statistical analysis" section is missing in the method part. For example, a lot of 3 or 4 way interaction analysis were done also with some post-hoc analysis. But it is not clear whether the interaction is significant or not. If not, then the post-hoc analysis may be not valid. Was multiple comparison correction applied? Which kind of correction was used? A summary table with statistical results will be useful to have an overview of the results. 2. For neurochemical data measured by MRS, authors used GABA/Glx ratio to evaluate the excitability in FPI. The increased excitability is concluded from reduced GABA/Glx. In fact, reduced GABA/Glx can be led by increases in Glx or decreases in GABA, or decreases in both but with more extent in GABA. Therefore, authors should quantify GABA and Glx separately, which will facilitate the interpretation of the results. Did authors acquire unsuppressed water spectra? You can quantify GABA and Glx separately, instead of using GABA/Glx. In this case, one can know if the change observed is driven by e.g. reductions in GABA or just increases in Glx. 3. Most of the mechanistic interpretation in the discussion is established on increased excitability in the FPI of anxious subjects. As commented above, these discussion part should be revised after looking into individual GABA and Glx results. 4. Another big issue is that the gender is largely biased between high-anixous (only 14 males) and non-anixous (all males) groups. The authors mentioned that gender has minor effect on the functional FPI engagement. They also claim that the structural connections and FPI neurochemical profile are unlikely affected by gender, which is lack of supporting evidences. At least, the gender effect on brain metabolites has been reported. If gender does have an effect on GABA and Glx, then authors should be careful that the observed difference may be dominated by the gender difference.
Minor comments: 1. Why left SMC, and right FPI and occipital lobe were investigated? Why not stay with the same side? 2. Please specify the snr in MRS data, is it the snr of edited spectra (referring to GABA peak? Or Glx peak?) or non-edited spectra? 3. What is the specific parameter measured from DWI for "connectivity"? This is a timely and important study. The findings are fascinating and add a new way to think about anxiety. I have a few suggestions, which, if responded, would, I think, improve the paper, and would allow me to support publication.
One concern is the relation between the frontal pole and amygdala. The authors write: "…the FPl has access to both medial and lateral cortical circuits through its extensive connections with other frontal, parietal and temporal association areas. In addition, FPl has direct access to information coming from the amygdala via the amygdalo-fugal bundle4,20. In contrast, macaque's amygdalae projects to medial but not lateral prefrontal regions24,25. Accordingly, recent work has suggested that FPl is involved in selecting emotional actions when different alternative options are available." The above text needs clarification. The implication is that the lateral frontal pole gets amygdala inputs via the amygdalo-fugal bundle. But the next sentence says that in monkeys the amygdala projects to the medial but not lateral frontal pole. This raises several anatomical questions.
First, how do we extrapolate from the monkey to the human lateral frontal pole? Although the monkey is our best source of cortical connectivity, the lateral pole of the monkey is considerably more primitive anatomically than humans. This is an important consideration. As emphasized in the introduction, this study is built on the observation that "the neuronal organization and connectivity profile of the human FPl have no homologue in rodents nor other primates" (page 3). More precisely, the lateral frontal pole (FPl) is a lateral subdivision of Brodmann area 10. This area indeed has a different neural organization and connectivity fingerprint as compared to the rest of human anterior prefrontal cortex and macaque frontal pole (BA10). Namely, in comparison to neighboring prefrontal areas, pyramidal neurons in the FPl are relatively sparse, yet they share more dense dendritic connections (Jacobs, 2001; Ramnani and Owen, 2004). Comparative studies have shown that macaque prefrontal cortex does not contain a region with a similar connectivity profile as human lateral frontal pole (Neubert et al., 2014). Therefore, we agree with this reviewer that it would be inappropriate to extrapolate connectivity of the macaque lateral frontal pole to humans.
We have adapted the text in order to clarify the issue: page 3, lines 70-75: More precisely, the human FPl has access to both medial and lateral cortical circuits through its extensive connections with other frontal, parietal and temporal association areas (Neubert et al., 2014). In addition, human FPl has direct access to information coming from the amygdala via the amygdalo- Second, the medial frontal pole, which is part of granular PFC is often thrown together with a/dysgranular medial PFC areas, especially in relation to the default mode network. I would suggest carefully evaluating the studies cited showing amygdala connections to granular medial frontal pole to rule out the possibility that that the findings are truly about granular medial frontal pole as opposed to adjacent a/dysgranular medial PFC.
Human granular medial frontal pole (FPm) shows a strong resemblance in terms of its connectivity profile to macaque BA 10, especially the latter's medial part (Neubert et al., 2014). This region has both efferent (Petrides and Pandya, 2007) and afferent connections with amygdala (Ghashghaei et al., 2007), although both are relatively sparse as compared to agranular medial frontal areas. Crucially, in macaques, medial BA10 receives stronger afferent as compared to efferent connections from amygdala, which separates it from a/dysgranular surrounding structures that show the opposite pattern (Ghashghaei et al., 2007). Although we cannot dissociate granular medial frontal polar cortex from adjacent a/dysgranular tissue, we think that is not a fundamental concern for this study, given its primary focus on the lateral Frontal Pole.
We have nuanced our framing of granular versus a/dysgranular -prefrontal connectivity: page 3 lines 62-70. Although connections between agranular regions of the medial prefrontal cortex are dominated by efferent fibers that project toward the amygdala complex, BA10 shows the opposite pattern. Namely, BA10 receives stronger input from the amygdala than vice-versa 24 . It is plausible that the same is true for lateral frontopolar cortex in humans..

Third, and related, there seems to be a contradiction about the amygdala and lateral FP. Which is is correct: (1) there are no inputs from amygdala to lateral FP, or (2) the amygdalo-fugal bundle carries amygdala inputs to the lateral FP? I would suggest exploring the literature to determine the purity of amygdalo-fugal bundle. Does it have fibers from other areas besides the amygdala?
It is relevant to consider species the statements are based on. We are not aware of any other regions that projects to the frontal pole via the amygdalofugal pathway. We have carefully reviewed the paper for clarity on the species on which anatomical statements are based. And added 'human' at several instances.
If all of the above is clarified, I would want to know more about which regions of the amygdala connect to lateral and medial FP. In general, the basolateral areas of the amygdala connect with neocortex and the central and medial regions connect with mesocortical, a/dysgraular medial cortex. Lateral and medial FP, being part of granular neocortex, might be expected to be connected with the basolateral amygdala. Knowing these details will be very important in understanding if and how amygdala connections with polar PFC contribute to anxiety.
Amygdala fibers projecting to the prefrontal cortex mainly stem from the basal or lateral nucleus (Ghashghaei et al., 2007;Aggleton et al., 2015), as these are the main output nuclei projecting to cortex. More specifically, the fibers projecting to BA10 in macaque originate mostly from the magnocellular division of the basal nucleus (Aggleton et al., 2015). At present, it is unknown whether this holds for human lateral Frontal Pole, but it is parsimonious to assume that this connectivity pattern of amygdala nuclei is preserved.
Interestingly, in macaques, amygdala connections with BA10 are dominated by bottom-up amygdala→BA10 projections rather than BA10→amygdala, a pattern that is opposite to that observed in agranular medial prefrontal cortices (Ghashghaei et al., 2007; Petrides and Pandya, 2007) but in line with the notion of the amygdalofugal path being the main output pathway of the amygdala complex.
As for the amygdalofugal pathway as a whole, it contains fibers stemming from basolateral but also -to a lesser extent -from the central nuclei of the amygdala, although the fibers stemming from the central nucleus probably do not extend all the way to prefrontal cortex but instead project to the Bed nucleus of the Stria Terminalis (BST) (Fox and Shackman, 2019).
We have added this information to the discussion, page 15, lines 368-377: The amygdalofugal bundle contains fibers stemming from both basal and lateral amygdala nuclei as well as the central nucleus. Although it is unclear where the amygdalofugal projections to FPl originate specifically, it is likely that these projections stem from the basal or lateral nucleus , given that most We agree that the subjective experience is a crucial component in disorders such as anxiety. In our case, we have included high-anxious individuals based on their self-reported anxiety, assessed via the LSAS questionnaire. We then used the STAI questionnaire to assess experienced symptoms, and those symptom scores correlate both with FPl involvement in the AA task and strength of the amygdalofugal projections to FPl. We have extended the discussion on anxiety symptoms and incorporated several of the suggested papers into the manuscript.
Putatively, the overexcitable FPl we observe in high-anxious, combined with stronger amygdala afferences when controlling emotional actions might make it difficult for anxious individuals to maintain their private convictions when conforming to social norms, a role attributed to FPl 42 .
Our results support recent suggestions that FPl arbitrates between imagined and veridical threat on the basis of magnocellular inputs(Cushing et al., 2023). Increased amygdala projections might make it difficult for anxious individuals to correctly attribute the assessed dangers projected to FPl to imagined or veridical threats. FPl's potential role as an arbitrator in threat imagery has mostly been described in terms of potential involvement of intrusive memories in PTSD (Cushing et al., 2023) and more generally fits recent views on the role of the FPl in emotional experience, such as anxiety, and its regulation (LeDoux, 2020). Interestingly, increased FPl activation during emotion control in our task can protect against the development of PTSD symptoms after trauma (Kaldewaij et al., 2021), and exposure therapy has been shown to restore frontopolar function in those PTSD patients that benefit from treatment(Fonzo et al., 2017). Here, they find that (4) FPl is more excitable in anxious vs. nonanxious individuals; and the magnitude of FPl excitability is differentially associated with the (behavioral) congruency effect in their task as a function of anxiety (such that non-anxious individuals have better emotional control with more excitable FPl; but vice versa for anxious folks). There are additional findings here that were not readily intuitive to me, which included that (5) the behavioral congruency effect was related to GABA/Glu in SMC in the non-anxious group, and that (6) the neural congruency effect in SMC correlated with FPl excitability only in the non-anxious group. When examining FPl structure, the authors found that (7) tamygdalofugal pathway/projections to FPl were stronger in high-anxious individuals and that this group difference was spatially specific to FPl and not amygdalofugal projections to medial PFC (BA24/25); and (8) that the association between amygdalofugal anatomy and emotional control changed between the groups, such that in the nonanxious group only, higher structural connectivity was associated with greater behavioral congruency effects (worse control(. They also note that (9) high anxiety participants show associations between amygdalofugal strength and neural congruency effect in mPFC and ACC. Finally, the authors find that amygdalofugal-FPl strength was associated with greater DLPFC, further corroborating finding (2).

Reviewer #2 (Remarks to the
On one hand, the quest of determining regional specificity in the PFC for the cognitive control of emotion is timely and in critical need of new data and careful analysis aimed at that goal. The authors have amassed an impressive amount of data to that end and have prioritized intra-subject reliability (collecting a large number of trials per subject; n>500), which is commendable and rarely done in affective neuroscience. Moreover, they use a task that is well validated by the past decade of their work, a body of work that had raised important questions regarding FPl "helping" or "hindering" regulation, which the present manuscript helps to answer. On the other hand, there were aspects of the current manuscript that I found challenging to parse-conceptually, and I also had a few methodological concerns, which if addressed, could strengthen an already impressive body of work. Figure 2C, I appreciate using Bayesian statistics, but another strong and easily interpretable test is whether there is a significant interaction between Group and the Congruency effect. Same question for Figure 2D: in other words, is FPl (incongruent) significantly more engaged for Non-anxious vs. Anxious, and DLPFC significantly more engaged for Anx vs. Non-Anx?

For finding in
We agree with the reviewer that a simple interaction analysis is important for interpretability of the findings displayed in Figure 2 and we indeed report it in the manuscript: Our whole brain and frontalcortex analyses contrasted congruency effects between high-anxious and non-anxious participants. On page 6 we report that there is a stronger congruency effect in high-anxious in dPFC (figure 2D) but no difference in FPl. However, because we did not observe a congruency effect in FPl within the highanxious group, an effect consistently observed in multiple earlier studies in non-anxious samples , we employed Bayesian statistics to assess the evidence for no effect. The Bayesian tests can be interpreted as a clarification of a null result observed in the Group by Congruency test the reviewer suggests for FPl.
We have changed our phrasing in the results section to clarify the logic of analyses: page 6, lines 139-146.
There was no interaction between group (high vs non-anxious) and congruency in FPl when correcting for voxels across the whole frontal cortex. However, separating this analysis for non-anxious and highanxious participants showed significant FPl activation in non-anxious but no statistically reliable congruency effect in the FPl for high-anxious participants, suggesting that they might rely less on FPl for control, figure 2C. To assess this possibility we used Bayesian t-test to clarify potential absence of FPl recruitment high-anxious individuals in the specific FPl territory recruited in healthy controls (figure 2C black circles) confirmed this observation, providing moderate evidence for the absence of this effect in the high-anxiety group, BF01 = 4.2.

An additional (potentially more direct) test of the authors' interesting conjecture regarding DLPFC vs. FPl engagement in the High Anx group is that the magnitude of Inc>Cong in DLPFC should be inversely correlated with the magnitude of Inc>Cong FPl, either in High Anx alone, or potentially across both groups. In other words, if DLPFC engagement is reflecting some sort of compensatory effect, then it should be inversely related to how much FPl is 'up to the task'. Is that the case? And/or is the delta DLPFC-FPl across subjects related to anxiety and/or the emotional congruency effect?
Across the two groups we observed a negative correlation between dPFC and FPl congruency effects, ρ(91)=-.22, r = .038, indeed confirming the reviewers suggestion and our speculation that the two are inversely related. Considering this relationship within each group separately suggests that this correlation is mainly driven by the anxious group, as those show a similar correlation between FPl and dPFC congruency, although not statistically reliable; ρ(50)= -.2, p = .15. The non-anxious group does not show relationship between FPl and dPFC neural congruency; ρ(39) = -.06, = .7.
We have added those findings to the results section: page 6, lines 156-160.
We have added this information to the results section, page 6-7 lines 160-163 .

The analysis that included Left SMC (Figures 3C & 3D) (also, why Left and not Right?) did not seem theoretically motivated until I got deep into the Methods section. If the authors consider that set of findings of equal import to the manuscript (vs. being in Supplementary) compared to FPl/DLPFC findings, then additional setting up of the theoretical framework-including prior findings-and stating the hypotheses in the Introduction would be warranted. As is, I found those findings quite difficult to follow and to integrate with FPl and DLPFC results.
This is an important point also brought forward by reviewer #3. The reason we take left sensorimotor cortex, rather than right SMC, is because our participants make approach and avoidance actions by moving a joystick towards or away from themselves with their right hand.
We agree that the manuscript would benefit from better introducing the role of FPl-SMC connectivity during emotional action control: In previous studies we have shown that when people select affectincongruent actions, increases in high-frequency neural activity in left SMC are phase-synchronized with low-frequency oscillations in right FPl (Bramson et al., 2018). Improving effective connectivity between FPl and SMC using dual-site tACS (in-phase vs anti-phase) can improve emotional-action control (Bramson et al., 2020a). Those observations were the starting point for the current studies. Therefore, we included left SMC as a region of interest for GABA/Glx voxel placement and consequent analyses. The findings reported in figures 3C&D specifically concern the relationship between FPl and SMC, not SMC alone. As we believe the FPl is involved in implementing emotional-action control by influencing left SMC in the current task, and FPl involvement is putatively reduced in the high-anxious individuals, this would likely result in a decoupling of FPl-SMC. The evidence supports this possibility, as visualized in figures 3C and 3D. We agree that this background could be better grounded in the introduction and results sections, which we have adapted accordingly.  (Fig 3B). It suggests there's a point at which FPl function/engagement is 'counter-productive' for Anx folks. I would have liked to see those findings more directly connected to DLPFC functon (Fig 2B)-are these two separate stories-i.e. are these effects impacting different individuals? Or are the High Anx individuals with more excitable FPl (less helpful) also the ones recruiting DLPFC more during the task (Inc> Con)? Can you enter those IVs (GABA/Glu, DLPFC engagement, FPl engagement, amygdalofugal pathway-FPl) in the same simultaneous regression model predicting Anx symptoms or the behavioral congruency effect to ascertain whether these neural metrics are explaining overlapping variance, or possibly exerting 'suppression' (in a regression framework) effects? (On suppression, see: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3780394/)

I found the reverse association between Gaba/Glu and the behavioral congruency effect intriguing
We have taken several steps to answer this question.
First, we have performed a Bayesian mixed effects model explaining performance based on a six-way interaction between Congruency (congruent/incongruent) * Group (non/high-anxious)*FPl BOLD * dPFC BOLD * FPl GABA/Glx * amygdalofugal-FPl connectivity. This model results in a significant Congruency*Group*FPl GABA/Glx interaction. However, several interactions we observed earlier are no longer statistically reliable. Given that Congruency*Group*amygdalofugal connectivity (figure 4B), and Congruency*Group*dPFC BOLD were significant when considered in isolation, we infer that dPFC congruency and amygdalofugal-FPl connectivity explain shared variance in the congruency effects on behavior, as the reviewer suggests. Accordingly, amygdalofugal-FPl projections correlate to dPFC engagement ( figure 4D).
However, while amygdalofugal-FPl connectivity and dPFC BOLD congruency explain shared variance, this shared variance is at least partly separate from the variance explained by FPl GABA/Glx. This is intuitive given that GABA/Glx ratio in FPl explains more variance in behavior in non-anxious (figure 3B), whereas high-anxious recruit dPFC rather than FPl. The extent of this compensatory recruitment depends on the strength of the amygdalofugal projection strength ( Figure 4D).
Given that the study was not set up to test six-way interactions (and therefore lacks statistical power), and given the relatively indirect nature of these observations, we have added these findings to the supplementary information section.

To assess which regressors might explain shared variance in the behavioral congruency we performed a Bayesian mixed effects model explaining performance based on a six-way interaction between Congruency (congruent/incongruent) * Group (non/high-anxious)*FPl engagement (BOLD effect congruentVSIncongruent) * dPFC engagement * FPl GABA/Glx * amygdalofugal-FPl connectivity.
This model results in a significant Congruency*Group*FPl GABA/Glx interaction; b = .2, CI [.04 .37]. However, several interactions we observed earlier are no longer statistically reliable. Given that Congruency*Group*amygdalofugal connectivity (figure 4B), and Congruency*Group*dPFC BOLD were significant when considered in isolation, we infer that dPFC congruency and amygdalofugal-FPl connectivity explain shared variance in the congruency effects on behavior. Accordingly, amygdalofugal-FPl projections correlate to dPFC engagement ( figure 4D).
However, while amygdalofugal-FPl connectivity and dPFC BOLD congruency explain shared variance, this shared variance is at least partly separate from the variance explained by FPl GABA/Glx. This is intuitive given that GABA/Glx ratio in FPl explains more variance in behavior in non-anxious (figure 3B), whereas high-anxious recruit dPFC rather than FPl. The extent of this compensatory recruitment depends on the strength of the amygdalofugal projection strength ( Figure 4D). This is an important caveat (see also comment #4 by reviewer #3) that was not addressed in the figures. As only 13 males are included in the high-anxious sample, it is not possible to reliably consider gender by adding it as a factor in our statistical model, and currently we do not have access to a suitable control sample (e.g. non-anxious females or high-anxious males) that also includes DWI or MRS data.

The gender imbalance across the samples raises questions about how to best address this
We have amended the discussion to more clearly point to potential caveats in structural connectivity due to gender differences pointed out by the reviewer. However, other work has shown that microstructural properties of amygdalofugal pathway do not seem to differ between sexes. Whether this is the case for amygdalofugal projections to lateral frontopolar cortex remains to be determined.
It could be argued that the current findings are biased by the skewed distribution of males and females across the high-anxious and non-anxious groups. For the neural and behavior congruency effects this is unlikely, as both male-only 3,30 and female only 63 studies using the AA task have shown FPl recruitment, and large scale mixed samples did not find differences between males and female participants in FPl engagement 9,64,65 . We also consider it unlikely that males and females differ in specific characteristics of FPl linked to emotional-action control, such as the structural connections from amygdala to FPl and FPl neurochemical profile, in the context of group-matched amygdalofugal projections to medial prefrontal cortices and excitability in SMC and V1 ( Figure 4C & supplementary  figure 2A;B). Although gender differences in the development of GABA concentration across the lifespan have been reported 66 , large scale studies comparing male and female participants did not show gender differences in the relationship between GABA and Glx in posterior 67 , or prefrontal cortex 68 . Further, although absolute levels of GABA might be different between males and females, the relationship between GABA and glutamate does not seem to vary across gender 69 . Gender differences have been shown in the relationship between anxiety and white-matter connectivity between amygdala and prefrontal cortices 70,71 . However, these were based on whole-bundle average estimates of structural integrity in the Uncinate Fasciculus, rather than differences in relative strength of projections. Microstructural assessment of amygdalofugal white-matter properties do not differ between males and females 72 . Future studies could more stringently test the potential influence of gender differences to amygdalofugal connectivity and FPl neural excitability.

Supplementary Figure 3 is quite novel and important; I was surprised to see it in Supplementary. In particular, I would be interested in knowing whether there is an interaction between region (Fpl vs. BA25 and 24) and Group (as for BA24/25 the association with Anx seems numerically reversed). It is also interesting that the amygdalofugal_FPl pathway correlates positively with anxiety, which warrants additional discussion.
This is an interesting suggestion, in particular given the knowledge on the difference in dominance of efferent versus afferent projections between area 24/25 and FPm (BA10) in macaques. Whereas area 24 and 25 have relatively more efferent connections towards the amygdala complex, FPm is a net receiver of projections from amygdala (Petrides and Pandya, 2007). However, we did not observe a region by group interaction in this dataset. The correlation between anxiety and amygdalofugal tract strength to FPl was placed in the supplements because we reasoned it was mainly a support for the group*amygdalofugal tract differences and its interaction with behavior shown in figure 4A;B. We have moved these results to figure 4 in the main manuscript.
In addition we discuss the relation between anxiety and the strength of the amygdalofugal-FPl path now in the discussion. Page 13-14, 330-332.
Putatively, the overexcitable FPl we observe in high-anxious, combined with stronger amygdala afferences when controlling emotional actions might make it difficult for anxious individuals to maintain their private convictions when conforming to social norms, a role attributed to FPl 42 . Figure 4D is very helpful for connecting and interpreting the structural-functional findings across the paper; but here I found Figure 4C difficult to integrate with the rest. Is there spatial overlap between 4C and 4D? How does 4C (medial PFC, also dorsal PFC, interaction with Group and amygdalofugal pathway) help with the interpretation of 4D (greater DLPFC engagement across groups for individuals with greater amygdalofugal pathway to FPl)?

7.
We agree that Figure 4D is more helpful than Figure 4C. Because the group differences in the interaction between amygdalofugal projections to FPl and neural congruency is further specified in the visualization in supplementary figure 3, we deem 4C superfluous and removed it. This creates space for showing data on the amygdalofugal projections to different medial prefrontal areas (point #6).

Intro Line 77: 'deviant recruitment'-awkward phrasing
We thank the reviewer for pointing to this phrase. The sentence now reads: Based on these findings, we reasoned that aberrant FPl recruitment might account for the difficulties experienced by individuals with anxiety in situations where they need to control emotional action tendencies.

Intro Line 95/Discussion Line 282: I am not sure the present data permit strong inferences regarding FPl afferents vs efferents; can the authors elaborate on this?
The reviewer is correct in their assessment, we cannot infer direction from the current data. Our suggestions were based on monkey granular prefrontal cortex mainly receiving amygdala efferences, although it also contains afferent fibers (Petrides and Pandya, 2007). Combined with our observation that increased amygdalofugal projections interfere with emotional-action control, we hypothesized stronger influence of affective information in action selection (Bramson et al., 2020b). However, this is conjecture.
We have toned down those claims in the introduction and discussion, page 4, lines 96-98: Furthermore, stronger amygdala connections, in the context of reduced FPl neuronal responsivity, significantly accounted for the anxiety-related shift towards those alternative control circuits in the frontal lobe.
Page 13, lines 308-313 There are three main findings. First, anxious individuals use dPFC, rather than FPl as their non-anxious peers, to implement control over emotional action tendencies. Second, FPl in anxious individuals might receive stronger input from the amygdala via more extensive amygdalofugal pathway connections, and the magnitude of that structural connection predicts the degree of FPl-dPFC shift during the implementation of emotional control.

Is there spatial overlap between the results shown in 2E and in 2C? If so, that would be worth indicating.
We have added a figure to the supplements showing the overlap between the neural congruency effects in the non-anxious (that show FPl activation) and the correlation between congruency effects and traitanxiety. See supplementary figure 1B. Figure 4; there is no 4E and I think the lettering is off by one throughout.

I believe there is an error when referring to
We have corrected this mistake throughout the manuscript.

I do wonder whether the authors examined differential amygdala activation profiles across these Anx vs. Non Anx groups and whether those relate to FPl structural, neurochemical, or functional profiles?
Potential differences in amygdala activity between groups, and their relationship with our variables of interest could have been seen in whole-brain analyses. We did not see any indication of groupdifferences in any of those tests.
6. This is at surface minor but it is important for interpretation. At times the 'congruence' effects seemed to refer to FPl's increased engagement to incongruent vs. congruent, but later it was referred to primarily as the facilitation of action by emotion. Keeping the labeling, terminology (and contrast direction) consistent throughout the manuscript would facilitate parsing through (and connecting) the present results.
We thank the reviewer for pointing this out and now consistently refer to 'neural congruency' or 'behavioral congruency effects' throughout the manuscript.

Reviewer #3 (Remarks to the Author):
Bramson et al. investigate the role of the brain circuit involving the lateral frontolpolar cortex (FPI), posterior parietal cortex, sensorimotor cortex and amygdala in emotion-action control in subjects with anxiety. They used magnetic resonance spectroscopy, diffusion weighted imaging and functional MRI to target FPI GABA/Glx levels, functional activation of FPI during a mild emotional challenge requiring emotional action control in high-anxious (n=52, 14 male) and non-anxious (n=41, all male) participants. Their main findings are the following: relative to non-anxious subjects, high-anxious participants use more dPFC in emotion-action control, have lower FPI GABA/Glx, and show stronger connectivity in amygdalofugal projection to FPI. They conclude that there is a functional-anatomical shift in emotion-action control in anxious subjects, and this shift is associated with the change in structural connectivity of amygdalofugal projections to FPl and overexcitability in FPI. Overall, I find that it is very nice to use a multimodal approach addressing the question from functional, structural and neurochemical aspects, which may provide mechanistic insights. I have a few major comments related to the study methodologies and design, which may affect the results, interpretation and conclusion.
Major comments:

1.
A "statistical analysis" section is missing in the method part. For example, a lot of 3 or 4 way interaction analysis were done also with some post-hoc analysis. But it is not clear whether the interaction is significant or not. If not, then the post-hoc analysis may be not valid. Was multiple comparison correction applied? Which kind of correction was used? A summary table with statistical results will be useful to have an overview of the results.
We agree that the paper would benefit from a statistical analysis section in the methods. The significant 3 and 4 way interactions are described in the text. To clarify those further, we describe them in the added statistical analysis paragraph in the methods section and a supplementary table that shows the models tested and the most important lower-order interactions. Most analyses were covered by separate hypotheses. Multiple-comparisons Bonferroni correction was used to correct across the three regions indexed with MRS when interpreting GABA/Glx ratio correlations with behavior (Supplementary figure 2B). Whole-brain results were corrected for multiple comparisons for all voxels in the brain or in the frontal lobe using cluster-correction using a cluster-forming threshold of z>2.3.

Page 21, lines 608-631
Analyses -statistics Statistical models testing behavioral congruency effects across and between groups, and derived models adding covariates were run in a step-wise fashion. We first compared correct responses between congruent and incongruent conditions between the different groups: Group (non-anxious vs highanxious) * congruency (congruent vs incongruent). We then extended this model in two iterations by adding estimates of excitability in a 4-way interaction: Group*congruency* FPl GABA/Glx * SMC GABA/Glx. Amygdalofugal tract strength was added in a separate three-way interaction: Group*Congruency*amygdalofugal tract strength. Significant interactions were assessed by interpreting lower-level interactions resulting from these same models, or post-hoc Spearman correlations. Full models and results for the most important interactions are presented in supplementary table 4. Correlations between neural excitability and behavioral congruency for the different groups were Bonferroni corrected for the three regions of interest. Analyses on functional MRI effects were cluster corrected using a cluster-threshold forming threshold of z>2.3 controlling either for all voxels in the brain (whole brain analyses) or all voxels in the frontal lobe.
2. For neurochemical data measured by MRS, authors used GABA/Glx ratio to evaluate the excitability in FPI. The increased excitability is concluded from reduced GABA/Glx. In fact, reduced GABA/Glx can be led by increases in Glx or decreases in GABA, or decreases in both but with more extent in GABA. Therefore, authors should quantify GABA and Glx separately, which will facilitate the interpretation of the results. Did authors acquire unsuppressed water spectra? You can quantify GABA and Glx separately, instead of using GABA/Glx. In this case, one can know if the change observed is driven by e.g. reductions in GABA or just increases in Glx.
Although our hypothesis was based on the GABA/Glx ratio (see also refs) we agree it is interesting to test for individual GABA and Glx effects. We only acquired MRS spectra with water suppression. It is therefore not possible to scale GABA and Glx estimates to water, giving a 'clean' estimate of both chemicals. However, we have added analyses relating both GABA and Glx estimates to Creatine (Cr), also present in our spectra. There is no statistical difference between non-anxious and high-anxious groups in either GABA/Cr and Glx/Cr ratio, suggesting that the ratio between inhibition and excitation is the important driver of our effects, not GABA or Glx in isolation. The 4-way interaction explaining behavioral performance (Group*Congruency*FPl excitability * SMC excitability), and the three-way interaction between Group*Congruency*FPl excitability also do not hold when testing GABA/Cr and Glx/Cr separately. Furthermore, neither GABA/Creatine nor Glx/Creatine correlate with behavioral congruency We have added this to the results section, page 9, lines 223-230.
To assess whether the results presented above can be attributed specifically to GABA or Glx alone, we repeated the main analyses by considering GABA and Glx independently, as a proportion of Creatine concentration. There was no difference in either FPl GABA/Cr ratio: t(89) = 1.14, p = .25, or FPl Glx/Cr ratio: t(89) = .29, p = .77 between groups. There were also no correlations between behavioral congruency effects and FPl GABA/Cr or Glx/Cr ratios; all ρ < .2, p > .16. Combined, these results suggest that the ratio between GABA and Glx is important for FPl-based emotional action control, and that it is specifically the ratio between inhibition and excitation in FPl that is different in high-anxious as compared to non-anxious individuals.
3. Most of the mechanistic interpretation in the discussion is established on increased excitability in the FPI of anxious subjects. As commented above, these discussion part should be revised after looking into individual GABA and Glx results.
Although the effects are indeed not the consequence of isolated effects of GABA or Glx but to the GABA/Glx ratio, we added discussion on the increased excitability interpretation. Page 16, lines 414-417.
The relationship between GABA/Glx ratio and behavioral congruency could not be attributed to effects of GABA (vs creatine) or Glx (vs creatine alone, suggesting emotional action control depends on relative inhibition/excitation in lateral frontal pole, rather than inhibitory or excitatory tone as such.

Another big issue is that the gender is largely biased between high-anxious (only 14 males) and
non-anxious (all males) groups. The authors mentioned that gender has minor effect on the functional FPI engagement. They also claim that the structural connections and FPI neurochemical profile are unlikely affected by gender, which is lack of supporting evidences. At least, the gender effect on brain metabolites has been reported. If gender does have an effect on GABA and Glx, then authors should be careful that the observed difference may be dominated by the gender difference.
We agree that we lack appropriate evidence for concluding that our results are not biased by gender and have toned down those claims, see also our response to reviewer #2. Further, we have added a discussion on potential gender differences in brain metabolites to the manuscript. Page 15-16, lines 387-405.
It could be argued that the current findings are biased by the skewed distribution of males and females across the high-anxious and non-anxious groups. For the neural and behavior congruency effects this is unlikely, as both male-only 3,30 and female only 63 studies using the AA task have shown FPl recruitment, and large scale mixed samples did not find differences between males and female participants in FPl engagement 9,64,65 . We also consider it unlikely that males and females differ in specific characteristics of FPl linked to emotional-action control, such as the structural connections from amygdala to FPl and FPl neurochemical profile, in the context of group-matched amygdalofugal projections to medial prefrontal cortices and excitability in SMC and V1 ( Figure 4C & supplementary figure 2A;B). Although gender differences in the development of GABA concentration across the lifespan have been reported 66 , large scale studies comparing male and female participants did not show gender differences in the relationship between GABA and Glx in posterior 67 , or prefrontal cortex 68 . Further, although absolute levels of GABA might be different between males and females, the relationship between GABA and glutamate does not seem to vary across gender 69 . Gender differences have been shown in the relationship between anxiety and white-matter connectivity between amygdala and prefrontal cortices 70,71 . However, these were based on whole-bundle average estimates of structural integrity in the Uncinate Fasciculus, rather than differences in relative strength of projections. Microstructural assessment of amygdalofugal white-matter properties do not differ between males and females 72 . Future studies could more stringently test the potential influence of gender differences to amygdalofugal connectivity and FPl neural excitability.
Minor comments: 1. Why left SMC, and right FPI and occipital lobe were investigated? Why not stay with the same side? Please see our response to reviewer #2, point #3: The reason we take left sensorimotor cortex, rather than right SMC, is because our participants make approach and avoidance actions by moving a joystick towards or away from themselves with their right hand. We are delighted to hear that our manuscript has been accepted, in principle, for publication in Nature Communications. We are pleased to read that the reviewers are mostly content with the changes we made to the manuscript. In response to remaining concerns, we have added a recognition in the discussion that the inclusions of only male participants in the non-anxious group is a limitation, we have made the requested changes to clarify terminology and we have expanded discussion of the importance of subjective feelings in anxiety.